JP2022000734A - Information processing unit, information processing method and information processing program - Google Patents

Abstract

To provide an information processing unit, an information processing method and an information processing program, which properly support a selection of a creator who is a producer of information.SOLUTION: A server device 10 comprises: an action history acquisition part 131B which acquires an action history being an action history of a plurality of first users and related to a specific person; an input number detection part 131A which detects the number of inputs of names that is the number of first users who have inputted input information including a name of the person, using first terminal devices (terminal devices 20) possessed by the first users; a first feature value calculation part 132 which calculates a first feature value indicating a general identification degree of the person based on the number of inputs of names; a second feature value calculation part 133 which calculates a second feature value indicating an anticipated value of a demand of a formed article formed by the person based on the action history; and a transmission part 135 which transmits the name of the person and first and second feature values of the person to a second terminal device (selector terminal 30) possessed by a prescribed second user.SELECTED DRAWING: Figure 2

Description

The present invention relates to an information processing apparatus, an information processing method, and an information processing program.

Conventionally, various information has been provided via the Internet. The information provided is not limited to those created by companies or organizations, but many are created by individuals (see, for example, Non-Patent Document 1).
Non-Patent Document 1 introduces a platform for creators active in various fields to post their own works and video contents. With such a platform, it is possible to easily provide and disseminate information created by individuals, and it is possible to support the expansion of the range of activities of creators.

"Increasing the value of" individual "dissemination-until Yahoo creates a platform for" creators "", [online], newsHACK, [Reiwa 2nd April 20], Internet (URL: https: // news. yahoo.co.jp/newshack/inside/creators.html)

By the way, in providing and disseminating information, it is important to select creators who create information that is of interest to readers and customers. Platforms such as the above non-patent document 1 can support the activities of creators by disclosing the information output by the creators, but they can grasp the readers and customers who are the recipients of the information and meet the demands of the readers and customers. It is not easy to select a creator in view of this. Conventionally, when selecting a creator, the platform administrator or the requester who requests the article from the creator needs to perform complicated work to find a creator that meets the demands of readers, customers, etc., and the work requires complicated work. , Experience, knowledge, sense, etc. were needed.

An object of the present invention is to provide an information processing device, an information processing method, and an information processing program that can suitably support the selection of a creator who is an information creator.

In the information processing apparatus of the present invention, the behavior history of a plurality of first users, the behavior history acquisition unit for acquiring the behavior history related to a specific person, and the input information including the name of the person are owned by the first user. The first feature value indicating the general recognition of a person is calculated based on the input number detection unit that detects the number of name inputs, which is the number of first users input by the first terminal device, and the number of name inputs. The feature value calculation unit, the second feature value calculation unit that calculates the second feature value indicating the expected value of demand for the article created by the person based on the action history, the name of the person, and the first of the person. It includes a transmission unit that transmits a feature value and a second feature value to a second terminal device owned by a predetermined second user.

In the present invention, it is possible to acquire the name of a person and calculate a plurality of feature values related to the person. This makes it possible to suitably support the selection of the creator who is the creator of the information.

The schematic diagram which shows the information processing system of one Embodiment of this invention. The block diagram which shows the schematic structure of the server apparatus of this embodiment. The flowchart which shows the information processing method in this embodiment. The figure which shows an example of the map generated in this embodiment. The figure which shows another example of the map generated in this embodiment.

Hereinafter, an embodiment according to the present invention will be described. In this embodiment, the selection of the author who is the creator of the article on the network will be described as an example.
[Overview of information processing system]
FIG. 1 is a schematic diagram showing an information processing system of the present embodiment.
As shown in FIG. 1, the information processing system of the present embodiment includes a server device 10 which is an information processing device, a plurality of terminal devices 20 (first terminal device) connected to the server device 10 via the Internet, and a plurality of terminal devices 20 (first terminal device). The selected person terminal 30 (second terminal device) is provided.
In the present embodiment, the server device 10 is a person (network) input by a terminal device 20 managed by a general user (corresponding to the first user in the present invention, hereinafter referred to as a "general user" for distinction). Obtain the name of the author candidate who is the creator of the above article, hereinafter referred to as "author candidate"). Further, the server device 10 calculates the first feature value and the second feature value for each author candidate whose name has been acquired. Further, the server device 10 generates a map by plotting the points corresponding to the calculated first feature value and the second feature value. Then, in the present embodiment, the server device 10 transmits the generated map to the selecter terminal 30.

The selecter terminal 30 is a terminal device managed by a user who is a creator's chooser (corresponding to a second user in the present invention, hereinafter referred to as a "selected user" for distinction). The selected user can be, for example, an example of an editor related to article creation, an administrator who manages a platform for posting information created by an article creator, an author, an author candidate, or the like.
In this embodiment, for convenience of explanation, an example in which the general user and the selected user are different users is shown, but the general user may be the selected user. In this case, the terminal device 20 also functions as the second terminal device of the present invention, and the server device 10 transmits the generated map to the terminal device 20 requesting the map.
Hereinafter, such an information processing system, particularly the server device 10, will be described in detail.

[Configuration of server device 10]
FIG. 2 is a block diagram showing a schematic configuration of the server device 10.
The server device 10 is composed of a general computer, and as shown in FIG. 2, includes each unit constituting the computer, such as a communication unit 11, a storage unit 12, and a processor 13. The number of computers constituting the server device 10 is not particularly limited. For example, the server device 10 may be configured by one computer, or a cloud server constructed by connecting a plurality of computers via a network may be used as the server device 10.
The communication unit 11 is connected to the Internet and communicates with each device such as the terminal device 20 and the selected person terminal 30 via the Internet.

The storage unit 12 records various information and information processing programs for controlling the server device 10.
Further, the storage unit 12 has a person name database (person name DB 121) that records the input author candidate name in association with the action history of the general user who is the input person, and a map database that records information about the generated map. (Map DB 122) and a database such as a user database (user DB 123) for recording user information of general users.
Here, an example in which various databases and information such as the person name DB 121, the map DB 122, and the user DB 123 are recorded in the storage unit 12 of the server device 10 will be shown, but the server device 10 can be connected to the server device 10 via a network. This information may be recorded in another data server or cloud storage.

In the person name DB 121, the author information associating the input name of the author candidate, the number of input names, and the action history of the general user who is the input person is recorded.
The name of the author candidate is a unique name of the author candidate input by the terminal device 20, and may be, for example, the name (real name) of the author candidate, or the common name, nickname, stage name, etc. of the author candidate. good. Further, a name dictionary for storing the real name, common name, nickname, stage name, etc. of these author candidates in association with each other may be stored in the storage unit 12. In this case, when the name of the author candidate is input in the terminal device 20, whether or not the input name is the same author candidate based on the name dictionary regardless of whether the input name is a real name or a common name. Can be determined. Therefore, the author information can be recorded as the same author candidate regardless of whether the input name is a real name or a common name.

The name input number is the number of general users who have input the name of the author candidate. For example, in an Internet search service, the number of author candidate names entered as keywords is recorded. The name input number may be the total number of general users who have input the name of the author candidate, or may be the real number of general users (the number of users who do not count duplication).
In addition, the above search services include a search service that returns search results of predetermined WEB contents for the entered keyword, a search service that searches for posted information and account information in social networking services (SNS), an internet shop, and the like. It also includes search services in product search.
In this embodiment, for the sake of simplification of the explanation, as an example, the number of author candidates entered as a keyword entered in the search service, that is, a person who has performed a search process using the author candidate as a keyword on the Internet. The information indicating how many people there are is used as the number of name inputs, but is not limited to this. For example, the number of general users who have posted the posted information including the name of the author candidate to the SNS or the like may be included in the number of name inputs.

The behavior history of a general user is a behavior history related to an author candidate of the author information. For example, the follow setting history for the account owned by the author candidate in the predetermined SNS, the response history in the SNS, the purchase history of the product or service related to the author candidate, the browsing history of the article related to the author candidate, the author. Search history of candidate names and registration history of membership services (e-mail magazines, online salons, fan clubs, etc.) related to author candidates. The follow history in SNS is, for example, whether or not it is set to notify the posting of the posted information or the update of the public profile when the author candidate posts the posted information or the author candidate updates the public profile. This is historical information indicating whether or not. Further, the response history is history information recorded when engagement such as retweet is performed on the posted information posted by the author candidate in the SNS.

The map DB 122 records the map generated by the server device 10 and information about the map. The details of the map will be described later. As the information about the map, the name of the author candidate and the first feature value and the second feature value calculated for each author candidate are recorded.

The user DB 123 records user information about a plurality of general users. The user information includes a user ID that identifies the user, user attributes, user behavior history, and the like.

The processor 13 is composed of an arithmetic circuit such as a CPU and a recording circuit such as a RAM. The processor 13 expands the information processing program recorded in the storage unit 12 into the RAM, and executes various processes in cooperation with the program expanded in the RAM.
Then, by reading and executing the information processing program, the processor 13 reads and executes the information processing program, and as shown in FIG. 2, the person DB update unit 131, the first feature value calculation unit 132, the second feature value calculation unit 133, the mapping unit 134, and the transmission. It functions as a part 135 and the like.

The person DB update unit 131 includes an input number detection unit 131A and an action history acquisition unit 131B.
The number of inputs detection unit 131A is the number of terminal devices 20 that have input the input information including the names of the author candidates, that is, the number of general users who have input the input information including the names of the author candidates (number of name inputs). Is detected. Then, the input number detection unit 131A counts up the number of name inputs of the author information corresponding to the input author candidate.
For example, when the server device 10 is a search server that provides a search service, the input number detection unit 131A counts search requests (input information) transmitted from each terminal device 20 using the name of the author candidate as a search keyword. By doing so, the number of name inputs can be detected. Further, when the search process using a predetermined application is executed in the terminal device 20, the name of the author candidate included as the search keyword of the search process may be set to be transmitted to the server device 10. , A transmission program that periodically transmits the name of the author candidate to the server device 10 may be introduced. Even in this case, the input number detection unit 131A extracts the names of the author candidates included in the search keywords by totaling the search keywords transmitted from each terminal device 20, and detects the number of name inputs for each author candidate. be able to.
Alternatively, the input number detection unit 131A may acquire the search keyword from the search server that performs the search process. For example, in the terminal device 20, when the search content is accessed using a web browser and the search process is performed, the search keyword is acquired from the search server that provides the search content, and the search keyword is aggregated to input the name. You may detect the number. Similarly, in the terminal device 20, when the search process is performed by the application that posts or browses to the SNS, the search keyword may be acquired from the search server provided by the SNS and the number of name inputs may be detected.

The action history acquisition unit 131B acquires the action history of a general user. This action history may be acquired from the user information of each general user recorded in the user DB 123 of the storage unit 12. Further, every time an action related to the author candidate is performed in the terminal device 20, the action history may be transmitted from the terminal device 20. Then, the action history acquisition unit 131B updates the action history of the author information corresponding to the author candidate.

The first feature value calculation unit 132 calculates the first feature value, which is a feature value indicating general recognition, for each author candidate. The calculation of the first feature value may be executed every time the number of name inputs of the author information is updated, may be executed at the timing of designation by the selected user, or may be executed at regular intervals. .. The details of the first feature value will be described later.

The second feature value calculation unit 133 calculates, for each author candidate, a second feature value indicating an expected value of demand for the created article created by the author candidate. Similar to the calculation of the first feature value, the calculation of the second feature value may be executed every time the number of name inputs of the author information is updated, or may be executed at the timing when the selection user specifies the value. It may be executed at regular intervals. Further, the calculation of the second feature value may be executed at the timing when the calculation of the first feature value is performed. The details of the second feature value will be described later.

The mapping unit 134 uses the first feature value calculated by the first feature value calculation unit 132 as the first index value and the second feature value calculated by the second feature value calculation unit 133 as the second index value. A map is generated in which points corresponding to a plurality of author candidates are plotted on two-axis coordinates with one index value as the first axis (horizontal axis) and the second index value as the second axis (vertical axis).
The transmission unit 135 transmits the map generated by the mapping unit 134 to the selecter terminal 30.
The detailed operation of each function configuration will be described later.

[Configuration of terminal device 20 and selected person terminal 30]
The terminal device 20 is a computer managed by a general user, and is composed of, for example, a smartphone, a tablet terminal, a personal computer, or the like. Further, the selected person terminal 30 is a computer managed by a selected user who is a creator's selected person, and is composed of, for example, a smartphone, a tablet terminal, a personal computer, or the like, like the terminal device 20. As described above, the terminal device 20 may function as the selected person terminal 30 of the present invention.
Although the illustration of the specific configuration of the terminal device 20 and the selected person terminal 30 is omitted, the terminal device 20 and the selected person terminal 30 have the basic configuration of a general computer. That is, the terminal device 20 and the selected person terminal 30 have an input operation unit that accepts the operation of the operator, a display that displays image information, a recording device that records various information, and an arithmetic circuit (CPU or the like) that performs arithmetic processing of various information. I have.

[Information processing method]
Next, the map generation and display method in the information processing system of the present embodiment will be described with particular focus on the information processing method in the server device 10.
FIG. 3 is a flowchart showing an information processing method in the present embodiment.
In order to generate a map, the person DB update unit 131 of the server device 10 first detects the number of input names of author candidates input by the terminal device 20 by the input number detection unit 131A (step S1: detection of the number of inputs). Step).
Next, the person DB update unit 131 of the server device 10 acquires the action history of a general user by the action history acquisition unit 131B (step S2: action history acquisition step).

The first feature value calculation unit 132 of the server device 10 calculates the first feature value based on the number of name inputs acquired by the input number detection unit 131A for each author candidate whose name is accumulated in the person name DB 121 (. Step S3: First feature value calculation step).
The first feature value is a feature value indicating general recognition, and is an index that becomes larger as it is widely recognized by general users. The first feature value calculation unit 132 uses, for example, the total number of name inputs of the author candidate, that is, the total number of search processes performed using the name of a certain author candidate, as the first feature value. calculate. The first feature value is not limited to this example as long as it is a value indicating general recognition. For example, the first feature value may be the ratio of the number of name inputs of the author candidate to the number of name inputs of all the author candidates.

The first feature value calculation unit 132 may calculate the first feature value for all the author candidates whose names are accumulated in the person name DB 121, such as only the author candidates having a predetermined number of searches or more. The first feature value may be calculated for the author candidate of the part. Further, the author candidate whose name is accumulated in the person name DB 121 may be a target for calculating the first feature value after performing a known narrowing process. For example, the author candidates whose names are accumulated in the person name DB 121 may be narrowed down according to the occupations of artists, athletes, actors, etc., and may be narrowed down according to attributes such as gender and age group. Also, the narrowing process may be performed according to the characteristics such as the theme and genre of the article created by the author candidate. Further, the narrowing process may be performed according to a combination of a plurality of types of conditions.

The second feature value calculation unit 133 of the server device 10 calculates the second feature value for each author candidate whose name is accumulated in the person name DB 121 based on the action history acquired by the action history acquisition unit 131B (step). S4: Second feature value calculation step).
The second feature value is a feature value indicating an expected value of demand for the created article created by the author candidate, and is an index that becomes larger as the expected demand is larger. As the second feature value, for example, there is a charge degree indicating the possibility that the reader charges the article created by the author candidate.
As the behavior history used for calculating the second feature value, as described above, the following behavior history can be mentioned.
・ Follow setting history for accounts owned by author candidates in SNS ・ Response history in the above SNS ・ Purchase history of products or services related to author candidates ・ Browsing history of articles related to author candidates ・ Name of author candidates Search history The second feature value calculation unit 133 calculates the second feature value based on at least one of the above-mentioned action histories. More specifically, the second feature value calculation unit 133 uses each action history as a variable, weights and adds each variable, and calculates the second feature value. The weight value of each variable may be predetermined or may be set by the selected user.
For example, for some authors candidate number N _A of general users who follow the author candidate in _SNS, the number of general users that originated the response information to the public profile and posts information of the author candidate N _B If the user number N _C was viewed by articles related to author _candidates, the number of users who searched for photopic the authors candidate N _D, the second characteristic value calculating unit 133, a second feature value I I = k calculated by _{1 × N A + k 2 ×} N B + k 3 × N C + k 4 × N D. Note that k ₁ , k ₂ , k ₃ , and k ₄ are weight values, respectively. This weight value may be calculated from the user who actually charged for the article of the author candidate, the follow history, the response history, the purchase history, the browsing history, and the search history of the user.
Further, in the present embodiment, since the action history is acquired from the terminal device 20 of the general user, whether the general user follows the author candidate on the SNS, returns the response information, and purchases the service of the author candidate. It is also possible to detect the number of users who take multiple actions with respect to author candidates such as. Therefore, the above variables _{N A, N B, N C} , in addition to the _{N D,} further, the number of users by a combination of a plurality of actions (e.g., follow the author candidates SNS, and transmits the response information to author candidate , And the number of users who purchased the service of the author candidate) and the weight value corresponding to the condition may be used.

The second feature value calculation unit 133 may calculate the second feature value for all the author candidates whose names are accumulated in the person name DB 121, such as only the author candidates having a predetermined number of searches or more. The second feature value may be calculated for the author candidate of the part. Further, the author candidate whose name is accumulated in the person name DB 121 may be a target for calculating the second feature value after performing a known narrowing process. Further, the second feature value calculation unit 133 may calculate the second feature value only for the author candidate whose first feature value is calculated by the first feature value calculation unit 132.

Then, the mapping unit 134 plots the points corresponding to each author candidate on the two-axis coordinates with the first feature value calculated in step S3 as the horizontal axis and the second feature value calculated in step S4 as the vertical axis. Then, a map is generated (step S5: mapping step). At this time, the mapping unit 134 may plot the points corresponding to all the author candidates for which the first feature value and the second feature value have been calculated, or may plot only the points corresponding to some of the author candidates. good. The author candidate to be mapped may be designated in advance by the selected user in the form of a range of the first feature value and the second feature value.

4 and 5 are diagrams showing an example of a map generated by the mapping unit 134. As shown in FIG. 4, the mapping unit 134 plots the points corresponding to each author candidate on the two-axis coordinates with the horizontal axis as the general recognition degree and the vertical axis as the billing degree, and generates a map.

In FIG. 4, author candidates with a high charge are mapped to the upper area of the map. Therefore, by making a selection with reference to the author candidates mapped to the upper area of the map, it is possible to select an author who is likely to be charged by the reader for the created article.
Further, in FIG. 4, an author candidate having a high general recognition is mapped to the area on the right side of the map. Therefore, it is possible to select an author with a high degree of general recognition by making a selection with reference to the author candidates mapped to the area on the right side of the map.

Further, as shown in FIG. 5, it is also possible to divide the map into a plurality of regions and select an author according to the correlation between the first feature value and the second feature value. For example, in the area A1 of FIG. 5, author candidates having a medium charge level but a relatively high general recognition level are mapped. Therefore, the persons N, M, and O mapped to the area A1 are candidates for authors with a medium degree of charge but a high degree of general recognition. Therefore, by selecting these persons as authors, it is possible to select authors who can stably acquire readers and customers based on general recognition.

Further, for example, the area A1 in FIG. 5 is mapped to an author candidate having a relatively high degree of general recognition and a relatively high degree of charge. Therefore, the persons J, K, and L mapped to the area A2 are author candidates with high general recognition and charge. Therefore, since these persons are unlikely to be authors in the future, such as having already created articles, etc., they can be useful information for persons to avoid when selecting authors.

Further, for example, an author candidate having a relatively high charge level and a relatively low general recognition level is mapped to the area A3 in FIG. Therefore, the persons A, B, and C mapped to the area A3 are candidate authors with a high degree of charge, although the general recognition is not high. Therefore, by selecting these persons as authors, it is possible to select authors whose core fans can be readers and customers.

The mapping unit 134 may superimpose at least one of the above-mentioned areas A1 to A3 on the map as a recommended area. The recommended area may be designated by the selected user, for example, in the form of a range of first feature values and second feature values based on predetermined conditions. The region A1 is a range in which the first feature value is equal to or higher than the first threshold value and the second feature value is equal to or higher than the second threshold value and is equal to or lower than the third threshold value. Is a range of the third threshold value or more, and the region A3 is a range in which the first feature value is less than the first threshold value and the second feature value is the fourth threshold value or more.
The mapping unit 134 may superimpose the recommended area as a frame image, or may highlight a person mapped to the recommended area.

With the map described above, the correlation between the general recognition level and the billing level can be visualized. Therefore, compared to the conventional complicated task of finding creators that meet the demands of readers and customers, author candidates are selected based on the balance between the general recognition of various general users and the charge level of general users. It becomes possible to grasp. Therefore, it is possible to dig up authors who have potential demand and select authors according to the purpose.
After step S5, the mapping unit 134 records the information about the generated map in the map DB 122.

The transmission unit 135 transmits the generated map to the selected person terminal 30 (step S6: transmission step). As a result, the selected person terminal 30 displays the map on the display (step S21).

[Action and effect of this embodiment]
The server device 10 of the present embodiment includes a storage unit 12 and a processor 13, and the processor 13 reads an information processing program stored in the storage unit 12 to read a person DB update unit 131 (input number detection unit 131A). And the action history acquisition unit 131B), the first feature value calculation unit 132, the second feature value calculation unit 133, and the transmission unit 135. The action history acquisition unit 131B of the person DB update unit 131 acquires the action history of a plurality of general users (first users) and is related to a specific person. The input number detection unit 131A of the person DB update unit 131 detects the number of name inputs, which is the number of general users who have input the input information including the name of the person in the terminal device 20 (first terminal device) owned by the general user. .. The first feature value calculation unit 132 calculates the first feature value indicating the general recognition of a person based on the number of name inputs. The second feature value calculation unit 133 calculates the second feature value indicating the expected value of the demand for the created article created by the person based on the action history. The transmission unit 135 transmits the name of the person and the first feature value and the second feature value of the person to the selected person terminal 30 (second terminal device) owned by the selected user (predetermined second user).

As a result, a plurality of feature values related to the person whose name is searched can be calculated, so that the selected user who manages the selecter terminal 30 can select a plurality of persons (author candidates) as the first feature value and the second feature. By examining based on the value, it can be used as a reference for selecting an author. Therefore, it is possible to suitably support the selection of the creator who is the creator of the information. In selecting such creators, tasks such as finding creators, creating attack lists for creators, and scouting creators are required. According to this embodiment, it also contributes to support and improvement of such tasks. It is possible to do.

In the present embodiment, the second feature value includes a charge degree indicating the possibility that the reader charges the created article.
By selecting an author in consideration of the degree of charge, it is possible to select an author who can make a core fan with a high degree of charge a reader or a customer.

In the present embodiment, the action history is a follow setting history targeting an account owned by a person who notifies a predetermined SNS of the posting process of posted information or the update process of a public profile performed by the person, and the person in the SNS. Sending history of posted information or response information to a person's public profile, purchase history of products or services related to the person, browsing history of articles related to the person, search history of the person's name, and membership system related to the person. Includes at least one registration history for the service.
As a result, it is possible to calculate a feature value that quantifies the possibility that the reader will be charged for the created article and use it for selecting an author.

In the present embodiment, a mapping unit 134 for generating a map plotting points corresponding to a person is provided on two-axis coordinates with the first feature value as the first axis and the second feature value as the second axis, and transmission is provided. The unit 135 transmits the generated map to the selecter terminal 30.
This makes it possible to visualize the correlation between the first feature value and the second feature value, and to grasp the author candidate based on the balance of each feature value.

In the present embodiment, the mapping unit 134 superimposes the range of the first feature value and the second feature value based on a predetermined condition on the map as a recommended area in the map.
This makes it possible to easily grasp author candidates that satisfy predetermined conditions.

In the present embodiment, the user is a selection person who selects the author of the created article based on the information transmitted by the transmission unit 135.
This can support the selection of authors for created articles.

[Modification example]
It should be noted that the present invention is not limited to the above-described embodiment, but also includes the modifications shown below to the extent that the object of the present invention can be achieved.

[Modification 1]
In the above embodiment, an example is shown in which the mapping unit 134 generates a map and the transmission unit 135 transmits the map from the server device 10 to the selected person terminal 30, but the information corresponding to the map is transmitted without generating the map. You may. Further, in addition to the map, information corresponding to the map may be transmitted.

For example, in the above embodiment, the transmission unit 135 may transmit the name of the author candidate and the first feature value and the second feature value of the person from the server device 10 to the selected person terminal 30.
In this case, the selected user who manages the selected person terminal 30 can refer to the selection of the author by examining a plurality of persons (author candidates) based on the first feature value and the second feature value. .. Therefore, the same effect as when the map is generated and transmitted can be obtained.

Further, for example, in the above embodiment, the first feature value and the second feature value are evaluated and ranked, and the transmission unit 135 includes the name of the author candidate, the rank of the first feature value calculated for the person concerned, and the rank of the first feature value. It may be transmitted from the server device 10 to the selected person terminal 30 in association with the rank of the second feature value.
In this case, the selected user who manages the selected person terminal 30 shall use a plurality of persons (author candidates) as a reference for selecting an author by examining them based on the ranks of the first feature value and the second feature value. Can be done. Therefore, the same effect as when the map is generated and transmitted can be obtained.

[Modification 2]
In the above embodiment, the processor 13 may further function as a person recommendation unit that extracts a person (author candidate) belonging to the range of the first feature value and the second feature value based on a predetermined condition as a recommended person. .. In this case, the transmission unit 135 may transmit the name of the extracted recommended person to the selected person terminal 30.
When extracting a recommended person, the person recommendation unit may refer to, for example, a person whose first feature value is equal to or higher than the first threshold value and whose second feature value belongs to the range of the second threshold value or higher and the third threshold value or lower (region A1 in FIG. 5). A person (a person mapped to) may be extracted as a recommended person, and a person whose first feature value is less than the first threshold value and whose second feature value belongs to the range of the fourth threshold value or more (mapped to the area A3 in FIG. 5). Person) may be extracted as a recommended person.
In this case, the selected user who manages the selected person terminal 30 can use the name of the recommended person itself as a reference for selecting the author. Therefore, the same effect as when the map is generated and transmitted can be obtained.

[Modification 3]
In the above embodiment, the processor 13 further obtains evaluation information for an article created by a person by an evaluation acquisition unit and a first feature value calculation unit 132 based on the evaluation information and a method of calculating a first feature value. (Ii) It may function as a change unit that changes at least one of the method of calculating the second feature value by the feature value calculation unit 133.
For example, the evaluation acquisition unit may acquire the name of the person and the evaluation information for the created article actually created by the person from the selected person terminal 30. This evaluation information includes the billing record showing the record of the reader's charge in the created article, and the author selected based on the processing by the server device 10 of the present embodiment actually creates the article, and the created article is included in the created article. This is information indicating how much the charge was made (total charge amount, number of charged users, charge amount per person). The selected user who manages the selected person terminal 30 transmits such evaluation information to the server device 10 via the selected person terminal 30.

The changing unit determines the calculation accuracy and accuracy of the first feature value and the second feature value based on the evaluation information, and based on the determination result, the calculation method of the first feature value by the first feature value calculation unit 132 and the calculation method of the first feature value. At least one of the method of calculating the second feature value by the second feature value calculation unit 133 may be changed.
For example, the change unit performs a search process based on the evaluation information, such as a counting method (total number or real number) of the number of name inputs used for calculating the first feature value by the first feature value calculation unit 132, and a count target (author candidate as a keyword). The number of general users who carried out the above, the number of general users who posted the posted information including the name of the author candidate to the SNS, etc.) may be changed.
Further, for example, the changing unit may change the combination of action histories (variables) used for calculating the second feature value by the second feature value calculating unit 133 or the weight value of each variable based on the evaluation information.

In this case, at least one of the method for calculating the first feature value and the method for calculating the second feature value can be tuned based on the results of the authors selected in the past. Therefore, it is possible to review and improve the author selection process by the server device 10.
As the evaluation information, instead of or in addition to the evaluation information for the articles created by the selected author, it is also possible to use the evaluation information of the selected author itself, for example, the evaluation information regarding the reliability and ability of the author. be.
Further, the map generated by the mapping unit 134 may be changed based on such evaluation information. For example, the range of the recommended area described with reference to FIG. 5 may be changed based on the evaluation information.

[Modification 4]
The map described in the above embodiment is an example, and the present invention is not limited to the above embodiment.
For example, the mapping unit 134 generated a map having the first feature value as the horizontal axis and the second feature value as the vertical axis, but calculated two types of second feature values based on combinations of variables different from each other. A map may be generated with each second feature value as the vertical axis and the horizontal axis.
Further, for example, when plotting the calculated first feature value and the point corresponding to the second feature value, the mapping unit 134 plots according to the acquisition number (search number) of the name of the person corresponding to the point. The display form of the points to be used may be changed. As the display form, for example, the color, size, and the like of the dots may be changed.

[Modification 5]
The method for calculating the second feature value described in the above embodiment is an example, and the present invention is not limited to the above embodiment.
For example, the second feature value calculation unit 133 may calculate the second feature value based on only some variables by setting at least one of the weight values of each variable to 0. Further, the second feature value calculation section 133, by all weight values except one weight value 0, for example, the variable N _A that indicates the number of general users who follow the author candidate in SNS The second feature value may be calculated based on one type of variable, such as calculating the second feature value based only on.

[Modification 6]
In the above embodiment, an example of acquiring the number of followers and the number of service purchases from the terminal device 20 as an action history of a general user is shown, but the present invention is not limited to the above embodiment.
For example, instead of the above-mentioned behavior history, or in addition to the above-mentioned behavior history, the number of followers of the author candidate may be obtained from the account owned by the author candidate in the SNS, and the related goods of the author candidate may be obtained from the shopping site. You may get the number of purchases. Further, for example, the number of views may be obtained from the news site, or the number of searches may be obtained from the search server.

10 ... Server device (information processing device), 12 ... Storage unit, 13 ... Processor, 20 ... Terminal device (first terminal device), 30 ... Selected person terminal (second terminal device), 121 ... Person name DB, 122 ... Map DB, 123 ... User DB, 131 ... Person DB update unit, 131A ... Input number detection unit, 131B ... Action history acquisition unit, 132 ... First feature value calculation unit, 133 ... Second feature value calculation unit, 134 ... Mapping Department, 135 ... Transmitter.

Claims (13)

An action history acquisition unit that acquires the action history related to a specific person, which is the action history of a plurality of first users.
An input number detection unit that detects the number of name inputs, which is the number of the first users who input the input information including the name of the person by the first terminal device owned by the first user.
A first feature value calculation unit that calculates a first feature value indicating the general recognition of the person based on the number of name inputs, and a first feature value calculation unit.
A second feature value calculation unit that calculates a second feature value indicating an expected value of demand for an article created by the person based on the action history, and a second feature value calculation unit.
Information characterized by comprising a transmission unit that transmits the name of the person, the first feature value and the second feature value of the person, to a second terminal device owned by a predetermined second user. Processing equipment. In the information processing apparatus according to claim 1,
The second feature value is an information processing apparatus characterized in that the created article includes a charge degree indicating the possibility that the reader charges. In the information processing apparatus according to claim 1 or 2.
The action history acquisition unit is an information processing device characterized in that the action history is acquired from the first terminal device. The information processing apparatus according to any one of claims 1 to 3.
The action history is a follow setting history for an account owned by the person who notifies a predetermined social networking service of the posting process of posted information or the update process of the public profile performed by the person, and the social networking service. The transmission history of the posted information posted by the person or the response information to the public profile of the person, the purchase history of products or services related to the person, the browsing history of articles related to the person, the name of the person. An information processing apparatus including at least one of a search history and a registration history of a membership service related to the person. The information processing apparatus according to any one of claims 1 to 4.
A mapping unit for generating a map in which points corresponding to a plurality of the persons are plotted on biaxial coordinates with the first feature value as the first axis and the second feature value as the second axis is provided.
The transmission unit is an information processing device characterized in that the map is transmitted to the second terminal device. In the information processing apparatus according to claim 5,
The mapping unit is an information processing apparatus characterized in that, in the map, the range of the first feature value and the second feature value based on a predetermined condition is superimposed on the map as a recommended area. The information processing apparatus according to any one of claims 1 to 6.
A person recommendation unit for extracting the person belonging to the range of the first feature value and the second feature value based on a predetermined condition as a recommended person is provided.
The transmission unit is an information processing device characterized in that the name of the recommended person is transmitted to the second terminal device. In the information processing apparatus according to claim 7,
The person recommendation unit includes the person whose first feature value is equal to or higher than the first threshold value and whose second feature value is equal to or higher than the second threshold value and whose first feature value is less than the first threshold value and whose first feature value is less than the first threshold value. (Ii) An information processing apparatus characterized in that at least one of the person whose feature value is equal to or higher than the fourth threshold value is extracted as the recommended person. The information processing apparatus according to any one of claims 1 to 8.
The evaluation acquisition department and the evaluation acquisition department for the evaluation information for the article created by the person mentioned above,
A change unit that changes at least one of the method of calculating the first feature value by the first feature value calculation unit and the method of calculating the second feature value by the second feature value calculation unit based on the evaluation information. An information processing device characterized by being equipped with. In the information processing apparatus according to claim 9,
The evaluation information is an information processing apparatus characterized in that the created article includes a billing record indicating a record charged by the reader. The information processing apparatus according to any one of claims 1 to 10.
The information processing apparatus, characterized in that the second user is a selection person who selects the author of the created article based on the information transmitted by the transmission unit. It is an information processing method that calculates feature values related to a person using a computer.
The computer includes an action history acquisition unit, an input number detection unit, a first feature value calculation unit, a second feature value calculation unit, and a transmission unit.
The action history acquisition unit is an action history acquisition step for acquiring the action history related to a specific person, which is the action history of a plurality of first users.
An input number detection step in which the input number detection unit detects the number of name inputs, which is the number of the first user who has input the input information including the name of the person in the first terminal device owned by the first user.
A first feature value calculation step in which the first feature value calculation unit calculates a first feature value indicating the general recognition of the person based on the number of input names.
The second feature value calculation unit calculates the second feature value indicating the expected value of the demand for the article created by the person based on the action history, and the second feature value calculation step.
The transmitting unit carries out a transmission step of transmitting the name of the person, the first feature value and the second feature value of the person, to a second terminal device owned by a predetermined second user. An information processing method characterized by that. An information processing program that can be read and executed by a computer.
An information processing program comprising the computer functioning as the information processing apparatus according to any one of claims 1 to 11.

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